A New Hope for DARPA OpTC
Résumé
This article addresses the challenges of using the DARPA OpTC dataset for intrusion detection system (IDS) research. While the dataset offers a rich combination of network and host data, its usability is hindered by a lack of an official event labeling and errors in logs data. We propose a two-fold solution: first, we identify and correct errors in the dataset, and second, we design and implement a comprehensive labeling methodology for attack-related events at both the network and host levels. Our corrected dataset, along with the labeling scripts, will be made publicly available to support reproducibility and further research. Additionally, we assess the impact of these labels and corrections on the effectiveness of graph-based machine learning IDS methods.
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